Closed-loop model parameter identification techniques for industrial model-based process controllers

ABSTRACT

A method includes obtaining closed-loop data associated with operation of an industrial process controller, where the industrial process controller is configured to control at least part of an industrial process using at least one model. The method also includes generating at least one noise model associated with the industrial process controller using at least some of the closed-loop data. The method further includes filtering the closed-loop data based on the at least one noise model. In addition, the method includes generating one or more model parameters for the industrial process controller using the filtered closed-loop data.

CROSS-REFERENCE TO RELATED APPLICATION AND PRIORITY CLAIM

This application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application No. 62/395,904 filed on Sep. 16, 2016,which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to industrial process control andautomation systems. More specifically, this disclosure relates toclosed-loop model parameter identification techniques for industrialmodel-based process controllers.

BACKGROUND

Industrial process control and automation systems are often used toautomate large and complex industrial processes. These types of controland automation systems routinely include process controllers and fielddevices like sensors and actuators. Some of the process controllerstypically receive measurements from the sensors and generate controlsignals for the actuators. Model-based industrial process controllersare one type of process controller routinely used to control theoperations of industrial processes. Model-based process controllerstypically use one or more models to mathematically represent how one ormore properties within an industrial process respond to changes made tothe industrial process.

SUMMARY

This disclosure provides closed-loop model parameter identificationtechniques for industrial model-based process controllers.

In a first embodiment, a method includes obtaining closed-loop dataassociated with operation of an industrial process controller, where theindustrial process controller is configured to control at least part ofan industrial process using at least one model. The method also includesgenerating at least one noise model associated with the industrialprocess controller using at least some of the closed-loop data. Themethod further includes filtering the closed-loop data based on the atleast one noise model. In addition, the method includes generating oneor more model parameters for the industrial process controller using thefiltered closed-loop data.

In a second embodiment, an apparatus includes at least one memoryconfigured to store closed-loop data associated with operation of anindustrial process controller that is configured to control at leastpart of an industrial process using at least one model. The apparatusalso includes at least one processing device configured to generate atleast one noise model associated with the industrial process controllerusing at least some of the closed-loop data. The at least one processingdevice is also configured to filter the closed-loop data based on the atleast one noise model and generate one or more model parameters for theindustrial process controller using the filtered closed-loop data.

In a third embodiment, a non-transitory computer readable mediumcontains instructions that, when executed by at least one processingdevice, cause the at least one processing device to obtain closed-loopdata associated with operation of an industrial process controller,where the industrial process controller is configured to control atleast part of an industrial process using at least one model. The mediumalso contains instructions that, when executed by the at least oneprocessing device, cause the at least one processing device to generateat least one noise model associated with the industrial processcontroller using at least some of the closed-loop data. The mediumfurther contains instructions that, when executed by the at least oneprocessing device, cause the at least one processing device to filterthe closed-loop data based on the at least one noise model. In addition,the medium contains instructions that, when executed by the at least oneprocessing device, cause the at least one processing device to generateone or more model parameters for the industrial process controller usingthe filtered closed-loop data.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates a first example industrial process control andautomation system according to this disclosure;

FIG. 2 illustrates a second example industrial process control andautomation system according to this disclosure;

FIG. 3 illustrates an example device supporting closed-loop modelparameter identification for industrial model-based process controllersaccording to this disclosure; and

FIG. 4 illustrates an example method for closed-loop model parameteridentification for industrial model-based process controllers accordingto this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 4, discussed below, and the various embodiments used todescribe the principles of the present invention in this patent documentare by way of illustration only and should not be construed in any wayto limit the scope of the invention. Those skilled in the art willunderstand that the principles of the invention may be implemented inany type of suitably arranged device or system.

As described above, model-based industrial process controllers are onetype of process controller routinely used to control the operations ofindustrial processes. Model-based process controllers typically use oneor more models to mathematically represent how one or more propertieswithin an industrial process respond to changes made to the industrialprocess. Model-based controllers typically require accurate models ofprocess behaviors in order to perform well. As conditions related to anindustrial process change, the models for that process typically need tobe updated or replaced in order to maintain good quality of control overthe process.

Closed-loop identification refers to a technique where process modelparameters are identified based on data from an industrial processoperating under closed-loop control. It is often desirable to be able toupdate or replace a process model based on closed-loop data since thiscan eliminate the need to turn off automatic controls and disturb theindustrial process to generate open-loop data. However, one problem withclosed-loop identification is that using standard identificationtechniques (such as those suitable for analysis of open-loop data) canresult in biased or inaccurate model parameter estimates, particularlywhen using direct identification methods without any knowledge abouttrue process and noise model structures.

This disclosure provides useful and versatile approaches for identifyingprocess model parameters using closed-loop process data while reducingor avoiding bias in the identified model parameters. These techniquesenable automatic closed-loop process model updating to be used formodel-based controllers, such as those used in machine-direction (MD)control of paper machines or other systems. These types of techniquescan provide various benefits depending on the implementation. Forexample, the proposed techniques can overcome biasing when performingclosed-loop identification, resulting in more accurate process modelsbeing generated. Moreover, with automatic closed-loop process modelupdating, model-based controls could be maintained to perform at thehighest level without needing to take those controls offline for plantexperiments. Further, these approaches can reduce the time and effortneeded in updating process models. In addition, overall controls can bekept functioning at high level at all times, reducing losses due to poorquality production.

FIG. 1 illustrates an example industrial process control and automationsystem 100 according to this disclosure. As shown in FIG. 1, the system100 includes various components that facilitate production or processingof at least one product or other material. For instance, the system 100is used here to facilitate control over components in one or multipleplants 101 a-101 n. Each plant 101 a-101 n represents one or moreprocessing facilities (or one or more portions thereof), such as one ormore manufacturing facilities for producing at least one product orother material. In general, each plant 101 a-101 n may implement one ormore processes and can individually or collectively be referred to as aprocess system. A process system generally represents any system orportion thereof configured to process one or more products or othermaterials in some manner.

In FIG. 1, the system 100 is implemented using the Purdue model ofprocess control. In the Purdue model, “Level 0” may include one or moresensors 102 a and one or more actuators 102 b. The sensors 102 a andactuators 102 b represent components in a process system that mayperform any of a wide variety of functions. For example, the sensors 102a could measure a wide variety of characteristics in the process system,such as temperature, pressure, or flow rate. Also, the actuators 102 bcould alter a wide variety of characteristics in the process system. Thesensors 102 a and actuators 102 b could represent any other oradditional components in any suitable process system. Each of thesensors 102 a includes any suitable structure for measuring one or morecharacteristics in a process system. Each of the actuators 102 bincludes any suitable structure for operating on or affecting one ormore conditions in a process system.

One or more networks 104 are coupled to the sensors 102 a and actuators102 b. The network 104 facilitates interaction with the sensors 102 aand actuators 102 b. For example, the network 104 could transportmeasurement data from the sensors 102 a and provide control signals tothe actuators 102 b. The network 104 could represent any suitablenetwork or combination of networks. As particular examples, the network104 could represent an Ethernet network, an electrical signal network(such as a HART or FOUNDATION FIELDBUS network), a pneumatic controlsignal network, or any other or additional type(s) of network(s).

In the Purdue model, “Level 1” includes one or more controllers 106,which are coupled to the network 104. Among other things, eachcontroller 106 may use the measurements from one or more sensors 102 ato control the operation of one or more actuators 102 b. Each controller106 includes any suitable structure for controlling one or more aspectsof a process system. As a particular example, each controller 106 couldrepresent a computing device running a real-time operating system.

Redundant networks 108 are coupled to the controllers 106. The networks108 facilitate interaction with the controllers 106, such as bytransporting data to and from the controllers 106. The networks 108could represent any suitable redundant networks. As particular examples,the networks 108 could represent a pair of Ethernet networks or aredundant pair of Ethernet networks, such as a FAULT TOLERANT ETHERNET(FTE) network from HONEYWELL INTERNATIONAL INC.

At least one switch/firewall 110 couples the networks 108 to twonetworks 112. The switch/firewall 110 may transport traffic from onenetwork to another. The switch/firewall 110 may also block traffic onone network from reaching another network. The switch/firewall 110includes any suitable structure for providing communication betweennetworks, such as a HONEYWELL CONTROL FIREWALL (CF9) device. Thenetworks 112 could represent any suitable networks, such as a pair ofEthernet networks or an FTE network.

In the Purdue model, “Level 2” may include one or more machine-levelcontrollers 114 coupled to the networks 112. The machine-levelcontrollers 114 perform various functions to support the operation andcontrol of the controllers 106, sensors 102 a, and actuators 102 b,which could be associated with a particular piece of industrialequipment (such as a boiler or other machine). For example, themachine-level controllers 114 could log information collected orgenerated by the controllers 106, such as measurement data from thesensors 102 a or control signals for the actuators 102 b. Themachine-level controllers 114 could also execute applications thatcontrol the operation of the controllers 106, thereby controlling theoperation of the actuators 102 b. In addition, the machine-levelcontrollers 114 could provide secure access to the controllers 106. Eachof the machine-level controllers 114 includes any suitable structure forproviding access to, control of, or operations related to a machine orother individual piece of equipment. Each of the machine-levelcontrollers 114 could, for example, represent a server computing devicerunning a MICROSOFT WINDOWS operating system. Although not shown,different machine-level controllers 114 could be used to controldifferent pieces of equipment in a process system (where each piece ofequipment is associated with one or more controllers 106, sensors 102 a,and actuators 102 b).

One or more operator stations 116 are coupled to the networks 112. Theoperator stations 116 represent computing or communication devicesproviding user access to the machine-level controllers 114, which couldthen provide user access to the controllers 106 (and possibly thesensors 102 a and actuators 102 b). As particular examples, the operatorstations 116 could allow users to review the operational history of thesensors 102 a and actuators 102 b using information collected by thecontrollers 106 and/or the machine-level controllers 114. The operatorstations 116 could also allow the users to adjust the operation of thesensors 102 a, actuators 102 b, controllers 106, or machine-levelcontrollers 114. In addition, the operator stations 116 could receiveand display warnings, alerts, or other messages or displays generated bythe controllers 106 or the machine-level controllers 114. Each of theoperator stations 116 includes any suitable structure for supportinguser access and control of one or more components in the system 100.Each of the operator stations 116 could, for example, represent acomputing device running a MICROSOFT WINDOWS operating system.

At least one router/firewall 118 couples the networks 112 to twonetworks 120. The router/firewall 118 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The networks 120 could represent anysuitable networks, such as a pair of Ethernet networks or an FTEnetwork.

In the Purdue model, “Level 3” may include one or more unit-levelcontrollers 122 coupled to the networks 120. Each unit-level controller122 is typically associated with a unit in a process system, whichrepresents a collection of different machines operating together toimplement at least part of a process. The unit-level controllers 122perform various functions to support the operation and control ofcomponents in the lower levels. For example, the unit-level controllers122 could log information collected or generated by the components inthe lower levels, execute applications that control the components inthe lower levels, and provide secure access to the components in thelower levels. Each of the unit-level controllers 122 includes anysuitable structure for providing access to, control of, or operationsrelated to one or more machines or other pieces of equipment in aprocess unit. Each of the unit-level controllers 122 could, for example,represent a server computing device running a MICROSOFT WINDOWSoperating system. Although not shown, different unit-level controllers122 could be used to control different units in a process system (whereeach unit is associated with one or more machine-level controllers 114,controllers 106, sensors 102 a, and actuators 102 b).

Access to the unit-level controllers 122 may be provided by one or moreoperator stations 124. Each of the operator stations 124 includes anysuitable structure for supporting user access and control of one or morecomponents in the system 100. Each of the operator stations 124 could,for example, represent a computing device running a MICROSOFT WINDOWSoperating system.

At least one router/firewall 126 couples the networks 120 to twonetworks 128. The router/firewall 126 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The networks 128 could represent anysuitable networks, such as a pair of Ethernet networks or an FTEnetwork.

In the Purdue model, “Level 4” may include one or more plant-levelcontrollers 130 coupled to the networks 128. Each plant-level controller130 is typically associated with one of the plants 101 a-101 n, whichmay include one or more process units that implement the same, similar,or different processes. The plant-level controllers 130 perform variousfunctions to support the operation and control of components in thelower levels. As particular examples, the plant-level controller 130could execute one or more manufacturing execution system (MES)applications, scheduling applications, or other or additional plant orprocess control applications. Each of the plant-level controllers 130includes any suitable structure for providing access to, control of, oroperations related to one or more process units in a process plant. Eachof the plant-level controllers 130 could, for example, represent aserver computing device running a MICROSOFT WINDOWS operating system.

Access to the plant-level controllers 130 may be provided by one or moreoperator stations 132. Each of the operator stations 132 includes anysuitable structure for supporting user access and control of one or morecomponents in the system 100. Each of the operator stations 132 could,for example, represent a computing device running a MICROSOFT WINDOWSoperating system.

At least one router/firewall 134 couples the networks 128 to one or morenetworks 136. The router/firewall 134 includes any suitable structurefor providing communication between networks, such as a secure router orcombination router/firewall. The network 136 could represent anysuitable network, such as an enterprise-wide Ethernet or other networkor all or a portion of a larger network (such as the Internet).

In the Purdue model, “Level 5” may include one or more enterprise-levelcontrollers 138 coupled to the network 136. Each enterprise-levelcontroller 138 is typically able to perform planning operations formultiple plants 101 a-101 n and to control various aspects of the plants101 a-101 n. The enterprise-level controllers 138 can also performvarious functions to support the operation and control of components inthe plants 101 a-101 n. As particular examples, the enterprise-levelcontroller 138 could execute one or more order processing applications,enterprise resource planning (ERP) applications, advanced planning andscheduling (APS) applications, or any other or additional enterprisecontrol applications. Each of the enterprise-level controllers 138includes any suitable structure for providing access to, control of, oroperations related to the control of one or more plants. Each of theenterprise-level controllers 138 could, for example, represent a servercomputing device running a MICROSOFT WINDOWS operating system. In thisdocument, the term “enterprise” refers to an organization having one ormore plants or other processing facilities to be managed. Note that if asingle plant 101 a is to be managed, the functionality of theenterprise-level controller 138 could be incorporated into theplant-level controller 130.

Access to the enterprise-level controllers 138 may be provided by one ormore operator stations 140. Each of the operator stations 140 includesany suitable structure for supporting user access and control of one ormore components in the system 100. Each of the operator stations 140could, for example, represent a computing device running a MICROSOFTWINDOWS operating system.

A historian 142 is also coupled to the network 136 in this example. Thehistorian 142 could represent a component that stores variousinformation about the system 100. The historian 142 could, for example,store information used during production scheduling and optimization.The historian 142 represents any suitable structure for storing andfacilitating retrieval of information. Although shown as a singlecentralized component coupled to the network 136, the historian 142could be located elsewhere in the system 100, or multiple historianscould be distributed in different locations in the system 100.

At least one of the controllers shown in FIG. 1 could denote amodel-based controller that operates using one or more process models144. For example, each of the controllers 106 could operate using one ormore process models 144 to determine, based on measurements from one ormore sensors 102 a, how to adjust one or more actuators 102 b. In someembodiments, each model 144 associates one or more manipulated ordisturbance variables with one or more controlled variables. Acontrolled variable (CV) generally represents a variable that can bemeasured or inferred and that is ideally controlled to be at or near adesired setpoint or within a desired range of values. A manipulatedvariable (MV) generally represents a variable that can be adjusted inorder to alter one or more controlled variables. A disturbance variable(DV) generally denotes a variable whose value can be considered butcannot be controlled. As a simple example, a flow rate of materialthrough a pipe could denote a controlled variable, a valve opening for avalve that controls the flow rate of material could denote a manipulatedvariable, and an ambient temperature around the pipe or the valve coulddenote a disturbance variable.

As noted above, the process models 144 need to be reasonably accuratefor model-based controllers to operate effectively, and the processmodels 144 typically need to be updated or replaced as conditionsrelated to an industrial process change. However, it is often difficultto use routine operating data to identify process model parameters whenperforming standard identification techniques since the model parameterestimates can be biased or inaccurate.

In accordance with this disclosure, at least one component of the system100 includes a tool 146 that analyzes routine operating data for atleast one model-based controller in order to perform closed-loopidentification. Example closed-loop identification processes aredescribed below. The tool 146 could be implemented in any suitablemanner and using any suitable device. For example, the tool 146 couldreside on any of the controllers or operator stations shown in FIG. 1.The tool 146 could also reside on any other suitable device(s) in FIG.1, such as on a dedicated computing device. The tool 146 could beimplemented using any suitable hardware or any suitable combination ofhardware and software/firmware instructions. In particular embodiments,the tool 146 is implemented using software/firmware instructions thatare executed by at least one processor of a computing device.

Although FIG. 1 illustrates one example of an industrial process controland automation system 100, various changes may be made to FIG. 1. Forexample, a control and automation system could include any number ofsensors, actuators, controllers, servers, operator stations, networks,models, tools, and other components. Also, the makeup and arrangement ofthe system 100 in FIG. 1 is for illustration only. Components could beadded, omitted, combined, further subdivided, or placed in any othersuitable configuration according to particular needs. Further,particular functions have been described as being performed byparticular components of the system 100. This is for illustration only.In general, process control and automation systems are highlyconfigurable and can be configured in any suitable manner according toparticular needs. In addition, while FIG. 1 illustrates one exampleenvironment in which closed-loop identification for model parameters canbe used, this functionality can be used in any other suitable device orsystem.

FIG. 2 illustrates a second example industrial process control andautomation system 200 according to this disclosure. In particular, thesystem 200 of FIG. 2 denotes an example web manufacturing or processingsystem. As shown in FIG. 2, the system 200 includes a paper machine 202,a controller 204, and a network 206. The paper machine 202 includesvarious components used to produce a paper product, namely a paper web208 that is collected at a reel 210. The controller 204 monitors andcontrols the operation of the paper machine 202, which may help tomaintain or increase the quality of the paper web 208 produced by thepaper machine 202. The machine direction (MD) of the web 208 denotes thedirection along the (longer) length of the web 208, while the crossdirection (CD) of the web 208 denotes the direction along the (shorter)width of the web 208.

In this example, the paper machine 202 includes at least one headbox212, which distributes a pulp suspension uniformly across the machineonto a continuous moving wire screen or mesh 213. The pulp suspensionentering the headbox 212 may contain, for example, 0.2-3% wood fibers,fillers, and/or other materials, with the remainder of the suspensionbeing water. Arrays of drainage elements 214, such as vacuum boxes,remove as much water as possible to initiate the formation of the web208. An array of steam actuators 216 produces hot steam that penetratesthe paper web 208 and releases the latent heat of the steam into thepaper web 208. An array of rewet shower actuators 218 adds smalldroplets of water (which may be air atomized) onto the surface of thepaper web 208. The paper web 208 is then often passed through a calenderhaving several nips of counter-rotating rolls. Arrays of inductionheating actuators 220 heat the shell surfaces of various ones of theserolls.

Two additional actuators 222-224 are shown in FIG. 2. A thick stock flowactuator 222 controls the consistency of incoming stock received at theheadbox 212. A steam flow actuator 224 controls the amount of heattransferred to the paper web 208 from drying cylinders. The actuators222-224 could, for example, represent valves controlling the flow ofstock and steam, respectively. These actuators may be used forcontrolling the dry weight and moisture of the paper web 208. Additionalflow actuators may be used to control the proportions of different typesof pulp and filler material in the thick stock and to control theamounts of various additives (such as retention aid or dyes) that aremixed into the stock.

This represents a brief description of one type of paper machine 202that may be used to produce a paper product. Additional detailsregarding this type of paper machine 202 are well-known in the art andare not needed for an understanding of this disclosure. Also, whiledescribed as being used to manufacture a paper web, other types ofmachines for manufacturing or processing any suitable webs could beused.

In order to control the paper-making process, one or more properties ofthe paper web 208 may be continuously or repeatedly measured. The webproperties can be measured at one or various stages in the manufacturingprocess. This information may then be used to adjust the paper machine202, such as by adjusting various actuators within the paper machine202. This may help to compensate for any variations of the webproperties from desired targets, which may help to ensure the quality ofthe web 208. As shown in FIG. 2, the paper machine 202 includes one ormore scanners 226-228, each of which may include one or more sensors.Each scanner 226-228 is capable of measuring one or more characteristicsof the paper web 208. For example, each scanner 226-228 could includesensors for measuring the tension, caliper, moisture, anisotropy, basisweight, color, gloss, sheen, haze, surface features (such as roughness,topography, or orientation distributions of surface features), or anyother or additional characteristics of the paper web 208.

Each scanner 226-228 includes any suitable structure or structures formeasuring or detecting one or more characteristics of the paper web 208,such as one or more sets of sensors. The use of scanners represents oneparticular embodiment for measuring web properties. Other embodimentscould be used, such as those including one or more stationary sets orarrays of sensors, deployed in one or a few locations across the web ordeployed in a plurality of locations across the whole width of the websuch that substantially the entire web width is measured.

The controller 204 receives measurement data from the scanners 226-228and uses the data to control the paper machine 202. For example, thecontroller 204 may use the measurement data to adjust any of theactuators or other components of the paper machine 202. The controller204 includes any suitable structure for controlling the operation of atleast part of the paper machine 202, such as a computing device. Notethat while a single controller 204 is shown here, multiple controllers204 could be used, such as different controllers that control differentvariables of the web.

The network 206 is coupled to the controller 204 and various componentsof the paper machine 202 (such as the actuators and scanners). Thenetwork 206 facilitates communication between components of the system200. The network 206 represents any suitable network or combination ofnetworks facilitating communication between components in the system200. The network 206 could, for example, represent a wired or wirelessEthernet network, an electrical signal network (such as a HART orFOUNDATION FIELDBUS network), a pneumatic control signal network, or anyother or additional network(s).

The controller(s) 204 can operate to control one or more aspects of thepaper machine 202 using one or more models 230. For example, each model230 could associate one or more manipulated or disturbance variableswith one or more controlled variables. The controlled variablestypically include one or more properties of the web 208. The manipulatedvariables typically include setpoints, settings, or other values used byvarious actuators in the system 200.

In accordance with this disclosure, at least one component of the system200 includes a tool 232 that analyzes routine operating data forclosed-loop model parameter identification. Example closed-loopidentification processes are described below. The tool 232 could beimplemented in any suitable manner and using any suitable device, suchas when the tool 232 resides on the controller 204 or a dedicatedcomputing device. The tool 232 could be implemented using any suitablehardware or any suitable combination of hardware and software/firmwareinstructions, such as when the tool 232 is implemented usingsoftware/firmware instructions that are executed by at least oneprocessor of a computing device.

Although FIG. 2 illustrates another example of an industrial processcontrol and automation system 200, various changes may be made to FIG.2. For example, other systems could be used to produce other paper ornon-paper products. Also, while shown as including a single papermachine 202 with various components and a single controller 204, thesystem 200 could include any number of paper machines or other machineryhaving any suitable structure, and the system 200 could include anynumber of controllers. In addition, while FIG. 2 illustrates anotherexample environment in which closed-loop identification for modelparameters can be used, this functionality can be used in any othersuitable device or system.

FIG. 3 illustrates an example device 300 supporting closed-loop modelparameter identification for industrial model-based process controllersaccording to this disclosure. The device 300 could, for example,represent any of the devices in FIGS. 1 and 2 that can execute the tool146, 232. However, the device 300 could be used in any other suitablesystem, and the tool 146, 232 could be implemented using any othersuitable device.

As shown in FIG. 3, the device 300 includes at least one processingdevice 302, at least one storage device 304, at least one communicationsunit 306, and at least one input/output (I/O) unit 308. The processingdevice 302 executes instructions that may be loaded into a memory device310. The processing device 302 may include any suitable number(s) andtype(s) of processors or other devices in any suitable arrangement.Example types of processing devices 302 include microprocessors,microcontrollers, digital signal processors, field programmable gatearrays, application specific integrated circuits, and discrete logicdevices.

The memory device 310 and a persistent storage 312 are examples ofstorage devices 304, which represent any structure(s) capable of storingand facilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory device 310 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 312 may contain one or more components or devices supportinglonger-term storage of data, such as a read only memory, hard drive,Flash memory, or optical disc.

The communications unit 306 supports communications with other systemsor devices. For example, the communications unit 306 could include anetwork interface card or a wireless transceiver facilitatingcommunications over a wired or wireless network. The communications unit306 may support communications through any suitable physical or wirelesscommunication link(s).

The I/O unit 308 allows for input and output of data. For example, theI/O unit 308 may provide a connection for user input through a keyboard,mouse, keypad, touchscreen, or other suitable input device. The I/O unit308 may also send output to a display, printer, or other suitable outputdevice.

Although FIG. 3 illustrates one example of a device 300 supportingclosed-loop model parameter identification for industrial model-basedprocess controllers, various changes may be made to FIG. 3. For example,various components in FIG. 3 could be combined, further subdivided,rearranged, or omitted and additional components could be addedaccording to particular needs. Also, computing devices can come in awide variety of configurations, and FIG. 3 does not limit thisdisclosure to any particular configuration of computing device.

FIG. 4 illustrates an example method 400 for closed-loop model parameteridentification for industrial model-based process controllers accordingto this disclosure. For ease of explanation, the method 400 is describedbelow as being implemented using the device 300 of FIG. 3 in the systems100, 200 of FIGS. 1 and 2. However, the method 400 could be performedusing any suitable device and in any suitable system.

As shown in FIG. 4, closed-loop data associated with a model-basedindustrial process controller is obtained at step 402. This couldinclude, for example, the processing device 302 that executes the tool146, 232 obtaining data associated with operation of a model-basedcontroller (such as a controller 106 or 204) from that controller orfrom another device. If the tool 146, 232 is executed within thecontroller 106 or 204, this could include the processing device 302collecting the data during execution of control logic by the processcontroller. The collected data can include routine operating data thatis generated as the process controller executes its control logic andattempts to control at least one industrial process (or portionthereof), such as controlled variable measurements and manipulatedvariable adjustments.

The closed-loop data is analyzed to identify at least one disturbancemodel at step 404. This could include, for example, the processingdevice 302 that executes the tool 146, 232 performing a modelidentification algorithm using the data. In particular embodiments, thetool 146, 232 implements a high-order autoregressive with exogenousterms (ARX) model identification algorithm that can fully capture thenoise model dynamics without needing information about the true noisemodel. Part of the high-order ARX model identification algorithm caninclude identifying a noise model associated with noise related to theindustrial process.

The closed-loop data is filtered using an inverse of the disturbancemodel at step 406. This could include, for example, the processingdevice 302 that executes the tool 146, 232 using an inverse of thepreviously-identified disturbance model to filter the closed-loop data.

Model parameter estimates for a process model are estimated using thefiltered closed-loop data at step 408. This could include, for example,the processing device 302 that executes the tool 146, 232 performing amodel identification algorithm using the filtered data, such as anoutput-error (OE) model identification algorithm or other modelidentification algorithm.

The model parameters are used in some manner at step 410. This couldinclude, for example, the processing device 302 that executes the tool146, 232 generating a new process model or updating an existing processmodel and providing the new or updated process model to a processcontroller. If the tool 146, 232 is executed within the controller 106or 204, this could include the processing device 302 updating theprocess model used by control logic of the process controller.

In this way, the proposed techniques can reduce or eliminate bias thatcan occur in other identification methods, such as when a directclosed-loop identification method suffers from bias due to aninsufficient specification of a noise model. Moreover, this approach canbe applied to routine operating data in which external excitation maynot exist and thus is suitable for continuous process model monitoring.

Although FIG. 4 illustrates one example of a method 400 for closed-loopmodel parameter identification for industrial model-based processcontrollers, various changes may be made to FIG. 4. For example, whileshown as a series of steps, various steps in FIG. 4 could overlap, occurin parallel, occur in a different order, or occur any number of times.

Additional details regarding specific techniques for closed-loop processmodel identification are provided below. Note that the details providedbelow are examples only and that other implementations of the techniquesdescribed in this patent document could be used. Also note that whilespecific details are provided below (such as specific types of models orspecific model identification algorithms), other implementations of thetechniques described below could be used.

The following describes techniques for closed-loop process modelidentification with routine operating data, which can support functionssuch as process performance monitoring and model-plant mismatchdetection. A noise model is estimated from a high-order ARXidentification with closed-loop data, and an OE identification isperformed on input-output data that has been filtered by the inverse ofthe estimated noise model. The proposed techniques can reduce oreliminate bias that occurs with a direct closed-loop identificationmethod due to insufficient specification of the noise model. Moreover,the proposed techniques can be applied to routine operating data inwhich external excitation may not exist and thus are suitable forcontinuous process model monitoring. These techniques can giveconsistent estimates, and it can be shown that the parameter estimatoris normally distributed asymptotically in the sample size. Among otherthings, these techniques help to overcome bias issues while preservingthe techniques' versatility and simplicity in dealing with linear andnonlinear closed-loop data. An advantage of the proposed techniques istheir applicability to situations with or without external excitations.

Initial Considerations

Consider the following single-input single-output (SISO) Box-Jenkinstrue system:

S:y(t)=G ₀(q)u(t)+H ₀(q)e(t)   (1)

where y(t) and u(t) denote the measured output signal (CV) and an inputsignal (MV), respectively. The true process model G₀ denotes a stableminimum-phase rational transfer function that is assumed to have atleast one sample delay. The noise model H₀ denotes a monic, stable, andinversely-stable filter. The sequence e(t) denotes independent andidentically distributed Gaussian white noise with zero mean and varianceσ_(e) ².

In a closed loop, assume the input signal u(t) can be determined by thefollowing nonlinear mapping:

u(t)=k(t,u ^(t-1) , y ^(t01) , r(t))   (2)

where u^(t-1)=[u(t−1), u(t−2), . . . , u(1)] and y^(t-1) is definedanalogously. An external excitation signal r(t) can be either a dithersignal (normally added to an actuator site) or a setpoint signal. Notethat Equation (2) is a general representation of a controller's behaviorand covers both linear and nonlinear cases. A typical example of anonlinear controller is an MPC controller with active constraints. Fromthe perspective of system identification, the nonlinear controller showsan important benefit since it can increase the informativeness of theclosed-loop data.

Assume that all relevant signals in Equations (1) and (2) arequasi-stationary, meaning the following conditions hold:

Es(t)=m(t), |m(t)|≦C, ∀t   (3)

Es(t)s(t−τ)=R _(s)(τ), |R _(s)(τ)|≦C, ∀τ  (4)

where s(t) denotes a signal in Equations (1) and (2), C denotes aconstant, and E denotes a conventional expectation operator for randomvariables. A generalized expectation operator E that could be applicableto signals having both stochastic and deterministic components can beexpressed as:

$\begin{matrix}{{\overset{\_}{E}{s(t)}} = {\lim\limits_{N\rightarrow\infty}{\frac{1}{N}{\sum\limits_{t = 1}^{N}{{Es}(t)}}}}} & (5)\end{matrix}$

Also, define:

Z ^(N) ={u(1),y(1), . . . , u(N),y(N)}  (6)

to be a collection of sampled closed-loop data. To facilitatederivations, the following assumption is used by default unlessotherwise explicitly stated. The collected closed-loop input-output dataZ_(N) is informative enough for the selected model structures in therelevant closed-loop identification. When external excitation exists,this assumption could be guaranteed by imposing the external excitationsignal r(t) to be persistently exciting. For cases without externalexcitation, assume that the controller is complex enough to make thisassumption hold. Note that for the derivations below, assume that r(t)exists, but note that the results without external excitations can beeasily derived by setting r(t)=0.

For the prediction error method (PEM), a class of model structures canbe constructed to fit into this data set, parameterized byθ=[ρ^(T)γ^(T)]^(T)∈Ω_(θ) 532

^(n) ^(θ) , such that:

M:y(t)=G(q,p)u(t)+H(q,γ)e(t)   (7)

where ρ ∈ Ω_(ρ) ⊂

^(n) ^(ρ) and γ ∈ Ω_(γ) ⊂

^(n) ^(γ) are the parameter vectors of the process model and the noisemodel, respectively. Define Q_(θ), Q_(ρ), and Q_(γ) to be thecorresponding compact sets of parameters θ, ρ, and γ. Further supposethat the selected model structures G(q, ρ) and H(q, γ) are uniformlystable with respect to the parameter θ and that H(q, γ) is alsoinversely uniformly stable. Note that G_(ρ), G_(ρ)(q), and G(q, ρ) areused interchangeably if there is no risk of confusion.

It can be assumed that the true process model is contained in the set ofthe selected process model structures, meaning:

G ₀ ∈G

{G(q, ρ)|ρ ∈ Ω_(ρ)}  (8)

Also, assume that all relevant closed-loop transfer functions formed bythe selected family of model structures are uniformly stable under thecontroller in Equation (2). This condition also guarantees closed-loopstability of the true system since the true system is contained in theselected model structures. This assumption is valid from a practicalpoint of view since, for most industrial processes, a priori knowledgeof the process is often available.

It is well-known that if the selected noise structure also contains thetrue noise model, the direct identification method provides consistentestimates for both the process and noise model parameters, regardless ofwhether the experiment is closed-loop or open-loop. However, thisstatement is too stringent to hold in practice since the characteristicsof the noise are generally too complex to analyze or to identify anappropriate model structure. Thus, discrepancies between the true noisemodel and the selected noise model structure are inevitable. Moreover,for process control engineers or other personnel, the reliability of theidentified process model is often more important than the reliability ofthe noise model for controller design.

A direct consequence of this noise model mismatch is a biased estimateof the process model if the PEM is applied to closed-loop data. To bemore specific, taking a fixed noise model H*(q) as an instance, theprocess model parameter estimate can be expressed as:

$\begin{matrix}{\rho^{*} = {\arg \; {\min\limits_{\rho \in \Omega_{\rho}}{\frac{1}{2\pi}{\int_{- \pi}^{\pi}{\frac{{{{G_{0}\left( e^{j\; \omega} \right)} + {B\left( e^{j\; \omega} \right)} - {G_{\rho}e^{j\; \omega}}}}^{2}{\Phi_{u}(\omega)}}{{{H_{*}\left( e^{j\; \omega} \right)}}^{2}}d\; \omega}}}}}} & (9)\end{matrix}$

Here, B(e^(jω)) is the bias term and could be expressed as:

$\begin{matrix}{{B\left( e^{j\; \omega} \right)} = \frac{\left( {{H_{0}\left( e^{j\; \omega} \right)} - {H_{\theta}\left( e^{j\; \omega} \right)}} \right){\Phi_{ue}(\omega)}}{\Phi_{u}(\omega)}} & (10)\end{matrix}$

where Φ_(ue)(ω) denotes the cross-spectrum between the input and thenoise. For open-loop data, Φ_(ue)(ω)=0. Thus, an OE structure with afixed noise model can give an unbiased process model estimate. Asdescribed below, a two-step approach can therefore be used to resolvethe bias issue while maintaining other advantages of the directidentification method.

Closed-Loop ARX-OE Identification

The following describes a particular implementation of a closed-loopmodel parameter identification technique based on ARX-OE identification.The proposed closed-loop identification technique includes two generalsteps, namely high-order ARX modeling followed by closed-loop OEidentification using filtered input-output data.

First Step: High-Order ARX Modeling

Equation (1) above can be rewritten into an equivalent form as follows:

A ₀(q)y(t)=B ₀(q)u(t)+e(t)   (11)

where:

$\begin{matrix}{{{A_{0}(q)} = \frac{1}{H_{0}(q)}},{{B_{0}(q)} = \frac{G_{0}(q)}{H_{0}(q)}}} & (12)\end{matrix}$

Since H₀(q) is assumed to be inversely stable, A₀(q) and B₀(q) are alsostable. In most cases, the impulse response (IR) coefficients of A₀(q)and B₀(q) contain infinite terms, meaning:

$\begin{matrix}{{{A_{0}(q)} = {1 + {\sum\limits_{k = 1}^{\infty}{a_{k}^{0}q^{- k}}}}},{{B_{0}(q)} = {\sum\limits_{k = 1}^{\infty}{b_{k}^{0}q^{- k}}}}} & (13)\end{matrix}$

Thus, the original Box-Jenkins model can be represented by an ARX modelbut with an infinite number of parameters. A high-order ARX model cantherefore be used to fit into the closed-loop data as follows:

A(q,η _(n))y(t)=B(q,η _(n))u(t)+e(t)   (14)

Here, n is the selected order, and

$\begin{matrix}{{{A\left( {q,\eta_{n}} \right)} = {1 + {\sum\limits_{k = 1}^{n}{a_{k}q^{- k}}}}},{{B\left( {q,\eta_{n}} \right)} = {\sum\limits_{k = 1}^{n}{b_{k}q^{- k}}}}} & (15)\end{matrix}$

with:

η_(n)=[a₁, . . . , a_(n), b₁, . . . , b_(n)]^(T)   (16)

The IR coefficients may converge to zero after sufficient lags, and thehigh-order ARX model can capture the first few significant coefficients.For example, based on Equations (14) and (15), the parameter estimatesof the ARX model can be readily achieved by solving a least-squaresproblem. The estimates of the parameters could be defined as:

{circumflex over (η)}_(n)=[{circumflex over (α)}₁, . . . , {circumflexover (α)}_(n), {circumflex over (b)}₁, . . . , {circumflex over(b)}_(n)]^(T)   (17)

It is evident that the parameter estimates {circumflex over (η)}_(n)under Equations (14) and (15) can suffer from large variance, such asdue to the large number of parameters. One remedy for this issue is toadd regularization to the least-squares problem. It has been shown thata regularized regressor converges to a true regressor (withprobability 1) as the sample number tends to infinity. Moreover, the IRcoefficients of most practical noise models tend to decay very quickly,and a priori information can be used to choose a reasonable model orderfor the ARX identification in the first step.

For theoretical convenience, assume that (as the sample size N tends toinfinity) the order of the selected ARX structure is allowed to tend toinfinity but with a much slower increase rate than N. For the high-orderARX model in Equation (14), it holds that:

n(N)→∞, n(N)^(3+δ) /N=∞, as N→∞  (18)

where δ>0 is a constant. With this assumption, denote:

{circumflex over (η)}_(N)={circumflex over (N)}_(n(N))   (19)

to represent the estimates of the parameters in η_(n) when n is allowedto tend to infinity as a function of N. Also, define η₀ as a vectorstacking the infinite number of true parameters in the high-order ARXmodel, meaning:

η₀=[a₁ ⁰, . . . , a_(n) ⁰, b₁ ⁰, . . . , b_(n) ⁰, . . . ]^(T)   (20)

In the following discussion, A₀(q) and A(q, η₀) are usedinterchangeably, as are B₀(q) and B(q, η₀).

Note that, if the various assumptions described above hold, thefollowing can be obtained:

$\begin{matrix}{\left. {\sup\limits_{\omega}{{{A\left( {e^{j\; \omega},{\hat{\eta}}_{N}} \right)} - {A_{0}\left( e^{j\; \omega} \right)}}}}\rightarrow 0 \right.,{w \cdot p \cdot 1},\left. {{as}\mspace{14mu} N}\rightarrow\infty \right.} & (21)\end{matrix}$

Thus, asymptotically in both the sample number and the order of the ARXmodel, the estimate A(q, {circumflex over (η)}_(N)) of A₀(q) convergesalmost surely to the true value. Notice that this holds regardless ofwhether the data is open-loop or closed-loop, as long as thecorresponding assumptions can be satisfied.

Second Step: OE Modeling with Filtered Input-Output Data

In the second step of the proposed approach, OE model identification isperformed using filtered input and output signals. The filter can bechosen as the estimated A(q,{circumflex over (n)}_(N)) from the firststep. For ease of notation, the operation of filtering a signal s(t)using A(q,{circumflex over (η)}_(N)) can be defined as:

s(t,{circumflex over (η)} _(N))=A(q,{circumflex over (η)} _(N))s(t)  (22)

to show this explicit dependence. With this notation, the filtered inputand output signals can be expressed as follows:

y(t,{circumflex over (η)} _(N))=A(q,{circumflex over (η)} _(N))y(t),u(t,{circumflex over (η)} _(N))=A(q,{circumflex over (η)} _(N))u(t)  (23)

To estimate the process model, fit the following OE model to thefiltered input-output data:

y(t,{circumflex over (η)} _(N))=G(q,ρ)u(t,{circumflex over (η)}_(N))+e(t), ρ∈Ω_(p)   (24)

where a priori information related to the process model can be imposed,such as by using a first-order plus time-delay model for a papermachine. The one-step-ahead predictor of the above OE model can beexpressed as:

ŷ(t|t−1, ρ, {circumflex over (η)}_(N))=G(q,ρ)u(t,{circumflex over (η)}_(N))   (25)

The resulting prediction error can be expressed as:

$\begin{matrix}{{ɛ\left( {t,\rho,{\hat{\eta}}_{N}} \right)} = {{{y(t)} - {\hat{y}\left( {{t{t - 1}},{\hat{\eta}}_{N}} \right)}} = {{\left\lbrack {G_{0} - {G\left( {q,\rho} \right)}} \right\rbrack {u\left( {t,{\hat{\eta}}_{N}} \right)}} + {\frac{A\left( {q,{\hat{\eta}}_{N}} \right)}{A_{0}(q)}{e(t)}}}}} & (26)\end{matrix}$

For the prediction error method, the optimal parameter can be obtainedby minimizing the following objective function:

$\begin{matrix}{{\hat{\rho}}_{N} = {{\arg \; {\min\limits_{\rho \in \Omega_{\rho}}{V_{N}\left( {\rho,{\hat{\eta}}_{N}} \right)}}} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{\frac{1}{2}{ɛ^{2}\left( {t,\rho,{\hat{\eta}}_{N}} \right)}}}}}} & (27)\end{matrix}$

Solving the OE model identification in Equations (26) and (27) ofteninvolves nonconvex optimization, so the global minima in general may notbe guaranteed. Global minimum searching algorithms are not developed inthis document, but for details on this topic see Zhu et al., “TheBox-Jenkins Steiglitz-McBride algorithm,” Automatica 65 (2016), pp.170-182 (which is hereby incorporated by reference in its entirety).

In practice, proper selection of the ARX model could involve trial anderror or could be based on apriori knowledge of a process. Also, it canbe shown through asymptotic analysis that the estimate of the processmodel determined using the described ARX-OE technique approaches thetrue model as the sample size N tends to infinity. Based on that, asufficiently high order is adequate to give high-quality estimates of anoise model. The regularization added to the least-squares problem helpsto improve the accuracy of the parameter estimates.

Note that many online process monitoring tasks, such as model-plantmismatch detection for MPC, may involve closed-loop identification thatis performed based on routine operating data (where external excitationsignals may not exist). It is well-known that, if there is no externalexcitation, closed-loop identification using the prediction error methodcould result in an estimate of the inverse of the controller. This isbecause of the possibility of relating input-output data throughfeedback instead of through process and noise models. In particular,when r(t)=0, the output and input signals could be equivalent to:

y(t)=G ₀(q)u(t)+H ₀(q)e(t)   (28)

u(t)=−K(q)y(t)   (29)

The fitting error of estimating G_(o) is larger than zero, while thefitting error of estimating −1/K(q) between y(t) and u(t) is zero. Thus,the PEM takes the controller inverse as the estimate of the processsince the corresponding prediction error is the minimum. One solution tothis issue is imposing a time delay on the identification method, wherea longer time delay will help prevent estimating the inverse of thecontroller. A number of delay estimation approaches using closed-loopdata are available. Examples are described in Björklund et al., “Areview of time-delay estimation techniques,” Proceedings of the 42ndIEEE Conference on Decision and Control, vol. 3, 2003, pp. 2502-2507 andBabji et al., “Time-delay estimation in closed-loop processes usingaverage mutual information theory,” Control & Intelligent Systems, vol.37(3), 2009, pp. 176-182 (both of which are hereby incorporated byreference in their entirety). Time-delay estimation could occur beforeperforming the closed-loop ARX-OE identification technique.

It has also been discovered that closed-loop data without externalexcitation may still contain certain excitations, such as excitationsdetermined by the complexity of the feedback controller. The specificrelationships between the order of the regulator and that of the processmodel have been investigated, and it has been concluded that a morecomplicated controller and a large time-delay enrich the informativenessof the closed-loop data. Thus, nonlinear dynamics of a controller can befavored from this perspective. Fortunately, for many industrialprocesses controlled by MPC (such as paper machines), routine data isgenerally sufficiently exciting to apply the ARX-OE identificationtechnique, especially when the MPC operates with active constraints. Theregularization in the first step of high-order ARX modeling can alsoguarantee its safety in case of less informative data.

In addition, it is well-known that, for closed-loop identificationmethods that use external excitation signals, noise has a negativeeffect on parameter covariance. For the direct identification method, ithas been argued that such noise is favored since it can reduce thevariance. This argument also applies here since system identificationbased on routine operating data that may lack external excitation isbeing performed. Moreover, from the perspective of identifiability,larger noise variance may be desirable or preferred here since it mayactivate more MPC constraints and thus increase the order of persistentexcitation of the closed-loop data.

Summary

This disclosure has described novel closed-loop identificationtechniques that can correct bias inherent in direct identificationmethods due to insufficient specification of a noise model. A high-orderARX or other model is identified to obtain an estimate of the noisemodel. The input and output data is filtered using an inverse of theestimated noise model, and an OE model identification or other modelidentification occurs with the filtered input-output data to obtain aprocess model estimate. It can be shown that this closed-loop ARX-OEidentification approach can give consistent estimates and that theparameter estimator is asymptotically normally distributed. Thesetechniques are applicable in various situations, including those where acontroller is nonlinear and those where closed-loop data contains noexternal excitations. Therefore, these techniques exhibit greatpotential for functions such as controller performance monitoring andmodel-plant mismatch detection that involve process model identificationbased on routine operating data.

In some embodiments, various functions described in this patent documentare implemented or supported by a computer program that is formed fromcomputer readable program code and that is embodied in a computerreadable medium. The phrase “computer readable program code” includesany type of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive, a compactdisc (CD), a digital video disc (DVD), or any other type of memory. A“non-transitory” computer readable medium excludes wired, wireless,optical, or other communication links that transport transitoryelectrical or other signals. A non-transitory computer readable mediumincludes media where data can be permanently stored and media where datacan be stored and later overwritten, such as a rewritable optical discor an erasable storage device.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “application”and “program” refer to one or more computer programs, softwarecomponents, sets of instructions, procedures, functions, objects,classes, instances, related data, or a portion thereof adapted forimplementation in a suitable computer code (including source code,object code, or executable code). The term “communicate,” as well asderivatives thereof, encompasses both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,may mean to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The phrase “at least one of,” when used with a list of items,means that different combinations of one or more of the listed items maybe used, and only one item in the list may be needed. For example, “atleast one of: A, B, and C” includes any of the following combinations:A, B, C, A and B, A and C, B and C, and A and B and C.

The description in the present application should not be read asimplying that any particular element, step, or function is an essentialor critical element that must be included in the claim scope. The scopeof patented subject matter is defined only by the allowed claims.Moreover, none of the claims invokes 35 U. S.C. §112(f) with respect toany of the appended claims or claim elements unless the exact words“means for” or “step for” are explicitly used in the particular claim,followed by a participle phrase identifying a function. Use of termssuch as (but not limited to) “mechanism,” “module,” “device,” “unit,”“component,” “element,” “member,” “apparatus,” “machine,” “system,”“processor,” or “controller” within a claim is understood and intendedto refer to structures known to those skilled in the relevant art, asfurther modified or enhanced by the features of the claims themselves,and is not intended to invoke 35 U.S.C. §112(f).

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A method comprising: obtaining closed-loop dataassociated with operation of an industrial process controller, theindustrial process controller configured to control at least part of anindustrial process using at least one model; generating at least onenoise model associated with the industrial process controller using atleast some of the closed-loop data; filtering the closed-loop data basedon the at least one noise model; and generating one or more modelparameters for the industrial process controller using the filteredclosed-loop data.
 2. The method of claim 1, wherein: generating the atleast one noise model comprises performing a first model identificationprocess; and generating the one or more model parameters comprisesperforming a second model identification process.
 3. The method of claim2, wherein: the first model identification process comprises ahigh-order autoregressive with exogenous terms (ARX) modelidentification process; and the second model identification processcomprises an output-error (OE) model identification process.
 4. Themethod of claim 1, wherein filtering the closed-loop data comprisesusing an inverse of the at least one noise model.
 5. The method of claim1, further comprising at least one of: updating the at least one modelusing the one or more model parameters; and generating at least onesecond model using the one or more model parameters.
 6. The method ofclaim 5, further comprising providing the at least one updated model orthe at least one second model to the industrial process controller. 7.The method of claim 1, wherein generating the at least one noise modelcomprises solving a least-squares problem having added regularization.8. An apparatus comprising: at least one memory configured to storeclosed-loop data associated with operation of an industrial processcontroller that is configured to control at least part of an industrialprocess using at least one model; and at least one processing deviceconfigured to: generate at least one noise model associated with theindustrial process controller using at least some of the closed-loopdata; filter the closed-loop data based on the at least one noise model;and generate one or more model parameters for the industrial processcontroller using the filtered closed-loop data.
 9. The apparatus ofclaim 8, wherein: the at least one processing device is configured toperform a first model identification process to generate the at leastone noise model; and the at least one processing device is configured toperform a second model identification process to generate the one ormore model parameters.
 10. The apparatus of claim 9, wherein: the firstmodel identification process comprises a high-order autoregressive withexogenous terms (ARX) model identification process; and the second modelidentification process comprises an output-error (OE) modelidentification process.
 11. The apparatus of claim 8, wherein the atleast one processing device is configured to use an inverse of the atleast one noise model to filter the closed-loop data.
 12. The apparatusof claim 8, wherein the at least one processing device is furtherconfigured to at least one of: update the at least one model using theone or more model parameters; and generate at least one second modelusing the one or more model parameters.
 13. The apparatus of claim 12,wherein the at least one processing device is further configured toprovide the at least one updated model or the at least one second modelto the industrial process controller.
 14. The apparatus of claim 8,wherein the at least one processing device is configured to solve aleast-squares problem having added regularization to generate the atleast one noise model.
 15. A non-transitory computer readable mediumcontaining instructions that, when executed by at least one processingdevice, cause the at least one processing device to: obtain closed-loopdata associated with operation of an industrial process controller, theindustrial process controller configured to control at least part of anindustrial process using at least one model; generate at least one noisemodel associated with the industrial process controller using at leastsome of the closed-loop data; filter the closed-loop data based on theat least one noise model; and generate one or more model parameters forthe industrial process controller using the filtered closed-loop data.16. The non-transitory computer readable medium of claim 15, wherein:the instructions that when executed cause the at least one processingdevice to generate the at least one noise model comprise instructionsimplementing a first model identification process; and the instructionsthat when executed cause the at least one processing device to generatethe one or more model parameters comprise instructions implementing asecond model identification process.
 17. The non-transitory computerreadable medium of claim 16, wherein: the first model identificationprocess comprises a high-order autoregressive with exogenous terms (ARX)model identification process; and the second model identificationprocess comprises an output-error (OE) model identification process. 18.The non-transitory computer readable medium of claim 15, wherein theinstructions that when executed cause the at least one processing deviceto filter the closed-loop data comprise: instructions that when executedcause the at least one processing device to filter the closed-loop datausing an inverse of the at least one noise model.
 19. The non-transitorycomputer readable medium of claim 15, further containing instructionsthat when executed cause the at least one processing device to at leastone of: update the at least one model using the one or more modelparameters; and generate at least one second model using the one or moremodel parameters.
 20. The non-transitory computer readable medium ofclaim 15, wherein the instructions that when executed cause the at leastone processing device to generate the at least one noise model comprise:instructions that when executed cause the at least one processing deviceto solve a least-squares problem having added regularization.